examples/tutorials/flare_simulator.ipynb

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    "## Intro to the FL Simulator\n",
    "\n",
    "The [FL Simulator](https://nvflare.readthedocs.io/en/latest/user_guide/nvflare_cli/fl_simulator.html) runs a local simulation of a running NVFLARE FL deployment.  This allows researchers to test and debug an application without provisioning a real, distributed FL project. The FL Simulator runs a server and multiple clients in the same local process, with communication that mimics a real deployment.  This allows researchers to more quickly build out new components and jobs that can be directly used in a production deployment.\n",
    "\n",
    "### Setup\n",
    "The NVFlare [Getting Started Guide](https://nvflare.readthedocs.io/en/main/getting_started.html) provides instructions for setting up NVFlare on a local system or in a Docker image.  We've also cloned the NVFlare GitHub in our top-level working directory."
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    "### Running the FL Simulator\n",
    "\n",
    "FL Simulator usage can be displayed with the NVFlare CLI: `nvflare simulator -h`"
   ]
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    "!nvflare simulator -h"
   ]
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    "The two key arguments here are `-w WORKSPACE` and the `job_folder` argument.  For this example, we'll create a test workspace and use the job from the `examples/hello-world/hello-numpy-sag/jobs/hello-numpy-sag`.\n",
    "\n",
    "We also specify the number of clients with the `-n N_CLIENTS` argument, the number of threads `-t THREADS` over which to run the clients, and a GPU device to use.  Setting `-n 2 -t 2 -gpu 0`, we will run two clients in parallel, both using GPU device 0. The log ouput is defaulted to \"concise\", optionally set the `-l LOG_CONFIG` argument to see different log outputs."
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    "!mkdir -p hello-numpy-sag-workspace\n",
    "!nvflare simulator -w hello-numpy-sag-workspace -n 2 -t 2 -gpu 0 ../hello-world/hello-numpy-sag/jobs/hello-numpy-sag"
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    "Watch the output above for the server to signal the run has completed:\n",
    "```\n",
    "    ScatterAndGather - INFO - Finished ScatterAndGather Training.\n",
    "```"
   ]
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    "We can then check the contents of the `hello-numpy-sag-workspace` directory to see the job output."
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4250a4cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "!ls -al hello-numpy-sag-workspace"
   ]
  }
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